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Partha P. Mitra

Researcher at Cold Spring Harbor Laboratory

Publications -  259
Citations -  25946

Partha P. Mitra is an academic researcher from Cold Spring Harbor Laboratory. The author has contributed to research in topics: Brain atlas & Zebra finch. The author has an hindex of 65, co-authored 254 publications receiving 23569 citations. Previous affiliations of Partha P. Mitra include Max Planck Society & Australian Research Council.

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Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography

TL;DR: Spontaneous magnetoencephalographic activity was recorded in awake, healthy human controls and in patients suffering from neurogenic pain, tinnitus, Parkinson's disease, or depression, indicating the presence of a thalamocortical dysrhythmia which is responsible for all the above mentioned conditions.
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Analysis of Dynamic Brain Imaging Data

TL;DR: The development of a decomposition technique (space-frequency singular value decomposition) that is shown to be a useful means of characterizing the image data and an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources are developed.
Posted Content

Temporal structure in neuronal activity during working memory in Macaque parietal cortex

TL;DR: In this article, the temporal structure of single unit (SU) activity and simultaneously recorded local field potential (LFP) activity from area LIP in the inferior parietal lobe of two awake macaques during a memory-saccade task were investigated.
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Temporal structure in neuronal activity during working memory in macaque parietal cortex

TL;DR: The temporal structure of local field potential activity and spiking from area LIP in two awake macaques during a memory-saccade task was studied and it was found that LFP activity in parietal cortex discriminated between preferred and anti-preferred direction with approximately the same accuracy as the spike rate.
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Multiple neural spike train data analysis: state-of-the-art and future challenges.

TL;DR: Statistical methods for the analysis of multiple neural spike-train data are reviewed and future challenges for methodology research are discussed.